2022-09-30 2D Advection-diffusion and waves

Last time

• Implicit Runge-Kutta methods

• Exploring/discussing tradeoffs

• SciML benchmarks suite (DifferentialEquations.jl)

Today

• FD methods in 2D

• Cost profile

• The need for fast algebraic solvers

• Wave equation and Hamiltonians

• Symplectic integrators

using Plots
default(linewidth=3)
using LinearAlgebra
using SparseArrays

function my_spy(A)
    cmax = norm(vec(A), Inf)
    s = max(1, ceil(120 / size(A, 1)))
    spy(A, marker=(:square, s), c=:diverging_rainbow_bgymr_45_85_c67_n256, clims=(-cmax, cmax))
end
    
function plot_stability(Rz, title; xlims=(-2, 2), ylims=(-2, 2))
    x = LinRange(xlims[1], xlims[2], 100)
    y = LinRange(ylims[1], ylims[2], 100)
    heatmap(x, y, (x, y) -> abs(Rz(x + 1im*y)), c=:bwr, clims=(0, 2), aspect_ratio=:equal, title=title)
end

struct RKTable
    A::Matrix
    b::Vector
    c::Vector
    function RKTable(A, b)
        s = length(b)
        A = reshape(A, s, s)
        c = vec(sum(A, dims=2))
        new(A, b, c)
    end
end

function rk_stability(z, rk)
    s = length(rk.b)
    1 + z * rk.b' * ((I - z*rk.A) \ ones(s))
end

rk4 = RKTable([0 0 0 0; .5 0 0 0; 0 .5 0 0; 0 0 1 0], [1, 2, 2, 1] / 6)

function ode_rk_explicit(f, u0; tfinal=1., h=0.1, table=rk4)
    u = copy(u0)
    t = 0.
    n, s = length(u), length(table.c)
    fY = zeros(n, s)
    thist = [t]
    uhist = [u0]
    while t < tfinal
        tnext = min(t+h, tfinal)
        h = tnext - t
        for i in 1:s
            ti = t + h * table.c[i]
            Yi = u + h * sum(fY[:,1:i-1] * table.A[i,1:i-1], dims=2)
            fY[:,i] = f(ti, Yi)
        end
        u += h * fY * table.b
        t = tnext
        push!(thist, t)
        push!(uhist, u)
    end
    thist, hcat(uhist...)
end

ode_rk_explicit (generic function with 1 method)

Extending advection-diffusion to 2D

1 dimension

(20)\[\begin{align} u_t + (- \kappa u_x + w u)_x &= f(x) & \text{ on } \Omega &= (a,b) \\ u(a) &= g_D(a) & u'(b) &= g_N(b) \end{align}\]

• Cell Peclet number \(\mathrm{Pe}_h = \frac{\lvert w \rvert h}{\kappa}\)

  • \(\mathrm{Pe}_h \lesssim 1\) avoids oscillations

  • \(\mathrm{Pe}_h \gtrsim 1\) is non-stiff for time-dependent model

• Centered versus upwind for advection

• Need uniformly bounded \(\kappa \ge \epsilon > 0\)

• “Strong form” not defined at discontinuities in \(\kappa\)

  • Works okay using divergence form and fluxes at staggered points

2 dimensions

(21)\[\begin{align} u_t + \nabla\cdot\big(- \kappa \nabla u + \mathbf{w} u \big) &= f(x,y) & \text{ on } \Omega & \subset \mathbb R^2 \\ u|_{\Gamma_D} &= g_D(x,y) \\ (-\kappa \nabla u + \mathbf w u) \cdot \hat n|_{\Gamma_N} &= g_N(x,y) \end{align}\]

• \(\Omega\) is some well-connected open set (we will assume simply connected) and the Dirichlet boundary \(\Gamma_D \subset \partial \Omega\) is nonempty.

• Finite difference methods don’t have an elegant/flexible way to specify boundaries

• We’ll choose \(\Omega = (-1, 1) \times (-1, 1)\)

On finite difference grids

• Non-uniform grids can mesh “special” domains

  • Rare in 3D; overset grids, immersed boundary methods

• Concept of staggering is complicated/ambiguous

Time-independent advection-diffusion

Advection

\[ \nabla\cdot(\mathbf w u) = \mathbf w \cdot \nabla u + (\nabla\cdot \mathbf w) u\]

If we choose divergence-free \(\mathbf w\), we can use the stencil

\[\begin{split} \mathbf w \cdot \nabla u \approx \frac 1 h \begin{bmatrix} & w_2 & \\ -w_1 & & w_1 \\ & -w_2 & \end{bmatrix} \!:\! \begin{bmatrix} u_{i-1, j+1} & u_{i, j+1} & u_{i+1,j+1} \\ u_{i-1, j} & u_{i, j} & u_{i+1,j} \\ u_{i-1, j-1} & u_{i, j-1} & u_{i+1,j-1} \\ \end{bmatrix} \end{split}\]

Diffusion

\[ -\nabla\cdot(\kappa \nabla u) = -\kappa \nabla\cdot \nabla u - \nabla\kappa\cdot \nabla u\]

• When would you trust this decomposition?

• If we have constant \(\kappa\), we can write

  \[\begin{split} -\kappa \nabla\cdot \nabla u \approx \frac{\kappa}{h^2} \begin{bmatrix} & -1 & \\ -1 & 4 & -1 \\ & -1 & \end{bmatrix} \!:\! \begin{bmatrix} u_{i-1, j+1} & u_{i, j+1} & u_{i+1,j+1} \\ u_{i-1, j} & u_{i, j} & u_{i+1,j} \\ u_{i-1, j-1} & u_{i, j-1} & u_{i+1,j-1} \\ \end{bmatrix} \end{split}\]

Advection-diffusion in code

function advdiff_matrix(n; kappa=1, wind=[1, 1]/sqrt(2))
    h = 2 / n
    rows = Vector{Int64}()
    cols = Vector{Int64}()
    vals = Vector{Float64}()
    idx(i, j) = (i-1)*n + j
    stencil_advect = [-wind[1], -wind[2], 0, wind[1], wind[2]] / h
    stencil_diffuse = [-1, -1, 4, -1, -1] * kappa / h^2
    for i in 1:n
        for j in 1:n
            if i in [1, n] || j in [1, n]
                push!(rows, idx(i, j))
                push!(cols, idx(i, j))
                push!(vals, 1.)
            else
                append!(rows, repeat([idx(i,j)], 5))
                append!(cols, [idx(i-1,j), idx(i,j-1), idx(i,j), idx(i+1,j), idx(i,j+1)])
                append!(vals, stencil_advect + stencil_diffuse)
            end
        end
    end
    sparse(rows, cols, vals)
end

advdiff_matrix (generic function with 1 method)

Spy the matrix

A = advdiff_matrix(10, wind=[1, 3], kappa=.1)
my_spy(A)

A = advdiff_matrix(20, wind=[0, 0], kappa=.1)
ev = eigvals(Matrix(A))
scatter(real(ev), imag(ev))

Plot a solution

n = 100
x = LinRange(-1, 1, n)
y = x
f = cos.(pi*x/2) * cos.(pi*y/2)'
heatmap(x, y, f, aspect_ratio=:equal)

A = advdiff_matrix(n, wind=[3,1], kappa=.01)
u = A \ vec(f)
heatmap(x, y, reshape(u, n, n), aspect_ratio=:equal)

• What happens when advection dominates?

• As you refine the grid?

Cost breadown and optimization

using ProfileSVG
function assemble_and_solve(n)
    A = advdiff_matrix(n)
    x = LinRange(-1, 1, n)
    f = cos.(pi*x/2) * cos.(pi*x/2)'
    u = A \ vec(f)
end

@profview assemble_and_solve(200)

┌ Warning: The depth of this graph is 98, exceeding the `maxdepth` (=50).
│ The deeper frames will be truncated.
└ @ ProfileSVG /home/jed/.julia/packages/ProfileSVG/ecSyU/src/ProfileSVG.jl:275
┌ Warning: The depth of this graph is 98, exceeding the `maxdepth` (=50).
│ The deeper frames will be truncated.
└ @ ProfileSVG /home/jed/.julia/packages/ProfileSVG/ecSyU/src/ProfileSVG.jl:275

What’s left?

• Symmetric Dirichlet boundary conditions

• Symmetric Neumann boundary conditions

• Verification with method of manufactured solutions

• Non-uniform grids

• Upwinding for advection-dominated problems

• Variable coefficients

• Time-dependent problems

• Fast algebraic solvers

Gas equations of state

There are many ways to describe a gas

Name	variable	units
pressure	\(p\)	force/area
density	\(\rho\)	mass/volume
temperature	\(T\)	Kelvin
(specific) internal energy	\(e\)	energy/mass
entropy	\(s\)	energy/Kelvin

Equation of state

\[ \rho, e \mapsto p, T \]

Ideal gas

(22)\[\begin{align} p &= \rho R T & e &= e(T) \end{align}\]

\[ p = (\gamma - 1) \rho e \]

pressure(rho, T) = rho*T

contour(LinRange(0, 2, 30), LinRange(0, 2, 30), pressure, xlabel="\$\\rho\$", ylabel="\$T\$")

Conservation equations

Mass

Let \(\mathbf u\) be the fluid velocity. The mass flux (mass/time) moving through an area \(A\) is

\[ \int_A \rho \mathbf u \cdot \hat{\mathbf n} .\]

If mass is conserved in a volume \(V\) with surface \(A\), then the total mass inside the volume must evolve as

\[ \int_V \rho_t = \left( \int_V \rho \right)_t = - \underbrace{\int_A \rho\mathbf u \cdot \hat{\mathbf n}}_{\int_V \nabla\cdot (\rho\mathbf u)},\]

where we have applied the divergence theorem. Dropping the integrals over arbitrary volumes, we have the evolution equation for conservation of mass.

\[ \rho_t + \nabla\cdot (\rho \mathbf u) = 0 \]

Momentum

The momentum \(\rho \mathbf u\) has a flux that includes

• convection \(\rho \mathbf u \otimes \mathbf u\)

  • this is saying that each component of momentum is carried along in the vector velocity field

• pressure \(p I\)

• viscous \(-\boldsymbol\tau\)

A similar integral principle leads to the momentum equation

\[ (\rho \mathbf u)_t + \nabla\cdot\big[ \rho \mathbf u \otimes \mathbf u + p I - \boldsymbol \tau \big] = 0 \]

Simplifications

• Ignore viscous stress tensor \(\boldsymbol \tau\)

• Ignore energy equation (not yet written) and assume constant temperature

  • \(p = a^2 \rho\) where \(a\) is the acoustic wave speed

\[\begin{split}\begin{pmatrix} \rho \\ \rho \mathbf u \end{pmatrix}_t + \nabla\cdot \begin{bmatrix} \rho \mathbf u \\ \rho \mathbf u \otimes \mathbf u + \rho a^2 I \end{bmatrix} = 0 \end{split}\]

Linearization

Split each state variable into a mean state and a small fluctuation

• \(\rho = \bar\rho + \tilde\rho\)

• \(u = \bar u + \tilde u\)

Let \(\widetilde{\rho u} = (\bar\rho + \tilde\rho) (\bar u + \tilde u) - \bar\rho\bar u \approx \tilde \rho \bar u + \bar\rho \tilde u\), where we have dropped the second order term \(\tilde \rho\tilde u\) because both are assumed small.

We consider background state \(\bar u = 0\) and constant \(\bar\rho(x,y,t) = \bar\rho\). Then

\[\begin{split}\begin{pmatrix} \tilde \rho \\ \bar\rho \mathbf{\tilde u} \end{pmatrix}_t + \nabla\cdot \begin{bmatrix} \bar\rho \mathbf{\tilde u} \\ \tilde \rho a^2 I \end{bmatrix} = 0 \end{split}\]

Two forms of acoustic wave equation

Divide the momentum equation through by background density and dropping the tildes yields the standard form.

\[\begin{split}\begin{pmatrix} \rho \\ \mathbf u \end{pmatrix}_t + \nabla\cdot \begin{bmatrix} \bar\rho \mathbf u \\ \rho \frac{a^2}{\bar\rho} I \end{bmatrix} = 0 .\end{split}\]

Examine second equation

\[ \frac{a^2}{\bar\rho} \nabla\cdot\big[ \rho I \big] = \frac{a^2}{\bar\rho} \nabla \rho \]

and thus $$\begin{pmatrix} \rho \ \mathbf u \end{pmatrix}_t +

(23)\[\begin{bmatrix} & \bar\rho \nabla\cdot \\ \frac{a^2}{\bar\rho} \nabla & \\ \end{bmatrix}\]

(24)\[\begin{pmatrix} \rho \\ \mathbf u \end{pmatrix}\]

Let’s differentiate the first equation,

\[ \rho_{tt} + \bar\rho\nabla\cdot(\mathbf u_t) = 0\]

and substitute in the second equation

\[ \rho_{tt} = a^2 \nabla\cdot(\nabla \rho)\]

• Note: we had to assume these derivatives exist!

We can reduce this to a first order system as

\[\begin{split}\begin{pmatrix} \rho \\ \dot \rho \end{pmatrix}_t + \begin{bmatrix} & -I \\ -a^2 \nabla\cdot\nabla & \end{bmatrix} \begin{pmatrix} \rho \\ \dot\rho \end{pmatrix} = 0\end{split}\]

Question

• How is the problem size different?

• What might we be concerned about in choosing the second formulation?

Laplacian in periodic domain

function laplacian_matrix(n)
    h = 2 / n
    rows = Vector{Int64}()
    cols = Vector{Int64}()
    vals = Vector{Float64}()
    wrap(i) = (i + n - 1) % n + 1
    idx(i, j) = (wrap(i)-1)*n + wrap(j)
    stencil_diffuse = [-1, -1, 4, -1, -1] / h^2
    for i in 1:n
        for j in 1:n
            append!(rows, repeat([idx(i,j)], 5))
            append!(cols, [idx(i-1,j), idx(i,j-1), idx(i,j), idx(i+1,j), idx(i,j+1)])
            append!(vals, stencil_diffuse)
        end
    end
    sparse(rows, cols, vals)
end

laplacian_matrix (generic function with 1 method)

L = laplacian_matrix(10)
ev = eigvals(Matrix(L))
scatter(real(ev), imag(ev))

Wave operator

\[\begin{split}\begin{pmatrix} \rho \\ \dot \rho \end{pmatrix}_t = \begin{bmatrix} & I \\ a^2 \nabla\cdot\nabla & \end{bmatrix} \begin{pmatrix} \rho \\ \dot\rho \end{pmatrix}\end{split}\]

function wave_matrix(n; a=1)
    Z = spzeros(n^2, n^2)
    L = laplacian_matrix(n)
    [Z I; -a^2*L Z]
end
wave_matrix(2)

8×8 SparseMatrixCSC{Float64, Int64} with 16 stored entries:
   ⋅     ⋅     ⋅     ⋅   1.0   ⋅    ⋅    ⋅ 
   ⋅     ⋅     ⋅     ⋅    ⋅   1.0   ⋅    ⋅ 
   ⋅     ⋅     ⋅     ⋅    ⋅    ⋅   1.0   ⋅ 
   ⋅     ⋅     ⋅     ⋅    ⋅    ⋅    ⋅   1.0
 -4.0   2.0   2.0    ⋅    ⋅    ⋅    ⋅    ⋅ 
  2.0  -4.0    ⋅    2.0   ⋅    ⋅    ⋅    ⋅ 
  2.0    ⋅   -4.0   2.0   ⋅    ⋅    ⋅    ⋅ 
   ⋅    2.0   2.0  -4.0   ⋅    ⋅    ⋅    ⋅ 

A = wave_matrix(8; a=2) * .1
ev = eigvals(Matrix(A))
plot_stability(z -> rk_stability(z, rk4), "RK4", xlims=(-4, 4), ylims=(-4, 4))
scatter!(real(ev), imag(ev), color=:black)

Question: would forward Euler work?

Example 2D wave solver with RK4

n = 20
A = wave_matrix(n)
x = LinRange(-1, 1, n+1)[1:end-1]
y = x
rho0 = vec(exp.(-9*((x .+ 1e-4).^2 .+ y'.^2)))
sol0 = vcat(rho0, zero(rho0))
thist, solhist = ode_rk_explicit((t, sol) -> A * sol, sol0, h=.02)
size(solhist)

(800, 51)

@gif for tstep in 1:length(thist)
    rho = solhist[1:n^2, tstep]
    contour(x, y, reshape(rho, n, n), title="\$ t = $(thist[tstep])\$")
end

┌ Info: Saved animation to 
│   fn = /home/jed/cu/numpde/slides/tmp.gif
└ @ Plots /home/jed/.julia/packages/Plots/lW9ll/src/animation.jl:137

Accuracy, conservation of mass with RK4

thist, solhist = ode_rk_explicit((t, sol) -> A * sol, sol0, h=.05,
    tfinal=1)

tfinal = thist[end]
M = exp(Matrix(A*tfinal))
sol_exact = M * sol0
sol_final = solhist[:, end]
norm(sol_final - sol_exact)

0.020151117484358525

mass = vec(sum(solhist[1:n^2, :], dims=1))
plot(thist[2:end], mass[2:end] .- mass[1])

Conservation of energy with RK4

Hamiltonian structure

We can express the total energy for our system as a sum of kinetic and potential energy.

\[H(\rho, \dot\rho) = \underbrace{\frac 1 2 \int_\Omega (\dot\rho)^2}_{\text{kinetic}} + \underbrace{\frac{a^2}{2} \int_\Omega \lVert \nabla \rho \rVert^2}_{\text{potential}}\]

where we identify \(\rho\) as a generalized position and \(\dot\rho\) as generalized momentum. Hamilton’s equations state that the equations of motion are

\[\begin{split} \begin{pmatrix} \rho \\ \dot\rho \end{pmatrix}_t = \begin{bmatrix} \frac{\partial H}{\partial \dot\rho} \\ -\frac{\partial H}{\partial \rho} \end{bmatrix} = \begin{bmatrix} \dot\rho \\ - a^2 L \rho \end{bmatrix} \end{split}\]

where we have used the weak form to associate \(\int \nabla v \cdot \nabla u = v^T L u\).

function energy(sol, n)
    L = laplacian_matrix(n)
    rho = sol[1:end÷2]
    rhodot = sol[end÷2+1:end]
    kinetic = .5 * norm(rhodot)^2
    potential = .5 * rho' * L * rho
    kinetic + potential
end
ehist = [energy(solhist[:,i], n) for i in 1:length(thist)]
plot(thist, ehist)

Velocity Verlet integrator

function wave_verlet(n, u0; tfinal=1., h=0.1)
    L = laplacian_matrix(n)
    u = copy(u0)
    t = 0.
    thist = [t]
    uhist = [u0]
    irho = 1:n^2
    irhodot = n^2+1:2*n^2
    accel = -L * u[irho]
    while t < tfinal
        tnext = min(t+h, tfinal)
        h = tnext - t
        u[irho] += h * u[irhodot] + h^2/2 * accel
        accel_next = -L * u[irho]
        u[irhodot] += h/2 * (accel + accel_next)
        accel = accel_next
        t = tnext
        push!(thist, t)
        push!(uhist, copy(u))
    end
    thist, hcat(uhist...)
end

wave_verlet (generic function with 1 method)

thist, solhist = wave_verlet(n, sol0, h=.04)
@gif for tstep in 1:length(thist)
    rho = solhist[1:n^2, tstep]
    contour(x, y, reshape(rho, n, n), title="\$ t = $(thist[tstep])\$")
end

┌ Info: Saved animation to 
│   fn = /home/jed/cu/numpde/slides/tmp.gif
└ @ Plots /home/jed/.julia/packages/Plots/lW9ll/src/animation.jl:137

Accuracy and conservation for Verlet

thist, solhist = wave_verlet(n, sol0, h=.05, tfinal=50)
tfinal = thist[end]
M = exp(Matrix(A*tfinal))
sol_exact = M * sol0
sol_final = solhist[:, end]
@show norm(sol_final - sol_exact)

mass = vec(sum(solhist[1:n^2, :], dims=1))
plot(thist[2:end], mass[2:end] .- mass[1])

norm(sol_final - sol_exact) = 6.86250099252763

ehist = [energy(solhist[:,i], n) for i in 1:length(thist)]
plot(thist, ehist)

Notes on time integrators

• We need stability on the imaginary axis for our discretization (and the physical system)

• If the model is dissipative (e.g., we didn’t make the zero-viscosity assumption), then we need stability in the left half plane.

• The split form \(\rho, \rho\mathbf u\) form is usually used with (nonlinear) upwinding, and thus will have dissipation.

Runge-Kutta methods

• Easy to use, stability region designed for spatial discretization

• Energy drift generally present

Verlet/leapfrog/Newmark and symplectic integrators

• These preserve the “geometry of the Hamiltonian”

  • energy is not exactly conserved, but it doesn’t drift over time

  • such methods are called “symplectic integrators”

• May not have stability away from the imaginary axis (for dissipation)

• Most require a generalized position/momentum split, “canonical variables”